Artificial neural networks for in-silico experiments on perception

Here the potential use of artificial neural networks for the purpose of understanding the biological processes behind perception is investigated. Current work in computer vision is surveyed focusing on methods to determine how a neural network utilizes it's resources. Analogies between feature detectors in deep neural networks and signaling pathways in the human brain are made. With these analogies in mind, procedures are outlined for experiments on perception using the recurrent temporal restricted Boltzmann machine as an example. The potential use of these experiments to help explain disorders of human perception is then described.